Comments (8)
Hello, could you try to adapt https://github.com/IBCNServices/GENESIM/blob/master/constructors/treeconstructor.py#L318 to min_samples_splits = np.arange(2,20,1) to see if the problems is fixed? It appeart that it is trying Grid Search and tries value 1 which now generates an exception in a newer version of sklearn.
Alternatively, you can param_opt=False in the construct_classifier call.
About the exceptions from xgboost, where those warnings or real exceptions?
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Moreover, if specific algorithms fail, you can just remove them from the algorithms dictionary on top of the example script (https://github.com/IBCNServices/GENESIM/blob/master/example.py#L29) (this is a very easy solution ;) )
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could you try to adapt https://github.com/IBCNServices/GENESIM/blob/master/constructors/treeconstructor.py#L318 to min_samples_splits = np.arange(2,20,1) to see if the problems is fixed?
I changed what you suggested along with https://github.com/IBCNServices/GENESIM/blob/master/constructors/treeconstructor.py#L247. Here the argument min_samples_split was getting set to self.min_samples_leaf. I changed this to
self.dt = DecisionTreeClassifier(criterion=self.criterion, min_samples_leaf=self.min_samples_leaf,
min_samples_split=self.min_samples_split, max_depth=self.max_depth)
It worked after this.
About the exceptions from xgboost, where those warnings or real exceptions?
A set of warnings like the following.
UserWarning: fmin_l_bfgs_b terminated abnormally with the state: {'warnflag': 2, 'task': 'ABNORMAL_TERMINATION_IN_LNSRCH', 'grad': array([ 1.16287611e-05]), 'nit': 5, 'funcalls': 50}
Moreover, if specific algorithms fail, you can just remove them from the algorithms dictionary on top of the example script (https://github.com/IBCNServices/GENESIM/blob/master/example.py#L29) (this is a very easy solution ;) )
I tried that already :). I was actually running it on my windows machine and I ran into trouble in the QUEST trees where it passes arguments using the subprocess. Will try running it on ubuntu machine.
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I get the same warnings (it's from the https://github.com/fmfn/BayesianOptimization library)
I have never ran it before on a Windows machine, I hope you can get it to work! Can I ask for what goal you are trying to use my library?
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I have created an ensemble of decision trees out of a GBM algorithm. Now I want to combine them into one so that I could somehow visualize the output of my model and also present it. But now I am wondering how would i integrate my trees with your algorithm. For that i guess I'll have to convert them to a format of your decisiontree class or I could implement the mutation and cross over part on my own and get the final tree. Any suggestions are most welcome on this :)
And regarding running it on a windows, it definitely is a pain and I won't recommend it but I somehow got it setup. Still, the subprocess issue seems to be a road block on this.
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Hello, implementing the interface for GBM is a possibility (if you do it, definitely create a pull request for it). On the other hand, it can be done with minimal adaptation to the genetic_algorithm function (https://github.com/IBCNServices/GENESIM/blob/master/constructors/genesim.py#L378). Here, in the beginning of the function, an ensemble in constructed (tree_list), this can be removed from the function and added as a new parameter. Then you would just have to extract the decision trees, convert them to my decisiontree object and pass them along as a parameter.
There's an issue open for removing the ensemble construction from the genetic_algorithm function, so feel free to create a pull request again if you choose this way :)
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Any updates @naveenkaushik2504? Else I'm closing this issue
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No updates actually. I moved on to some other project. Will explore more on this later. Closing the issue.
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